Why $30k an acre is cheap
Executive Summary:
- Enhanced Productivity and Efficiency: AI-driven tools in agriculture, such as precision farming and livestock management systems, significantly improve crop yields and reduce operating costs through optimized resource use, disease detection, and data-informed decision-making.
- Sustainability and Climate Resilience: AI technologies enable sustainable farming practices, including efficient water and fertilizer use, reduced greenhouse gas emissions, and enhanced resilience to climate change and extreme weather conditions.
- Innovation in Supply Chain Management: AI applications in supply chains optimize logistics, reduce food waste, and increase transparency, ensuring food quality and minimizing losses from farm to consumer.
- Emerging Technologies and Growth Areas: Autonomous farming equipment, AI-powered vertical farms, and lab-grown meat exemplify cutting-edge innovations that align with global sustainability and food security priorities.
- Investment and Adoption Challenges: Despite the immense potential, barriers such as high costs, limited access in developing countries, and fragmented data systems need to be addressed to maximize AI’s impact in the agricultural sector.
AI is driving efficiency and productivity gains in every sector it’s applied, and agriculture is no different. But don’t take our word for it, let’s see what Land O’ Lakes CTO Teddy Bekele said on MIT’s podcast “Me, Myself and AI”:
Sam Ransbotham: So with all these data and artificial intelligence, machine learning models you’re doing, somehow you’ve gone in this fourth revolution or fourth … I can’t remember what you called it, fourth wave?
Teddy Bekele: Revolution. Yes.
Sam Ransbotham: What’s next?
Teddy Bekele: With some of the capabilities, both the biotechnology as well as the software technology component of it (AI and biotech combo) , there’s farmers that can get up to 540 bushels per acre.
Currently, a top producing Iowa acre creates around 180 bushels per acre. If Bekele is correct, production will increase 3x!
Recently, a prime ag acre in Iowa sold for a record $30,000. This seems extraordinarily high. Yet if the CTO from Land O’ Lakes CTO is correct, this land appears to be 2x or 3x undervalued. This is the potential for AI.
And this is critical as increases in global population lead to corresponding increases in food demand, and technological evolution in the agricultural sector plays a key role in meeting this need. Food inflation remains a serious problem, but AI can improve productivity, resilience and sustainability in farming practices, leading to greater yields, less resource wastage, and preventing food shortage issues. Consulting firm McKinsey explains in a 2024 article that the use of AI, in particular the increasing adoption of both analytical and generative AI, can lead to significant improvements both “on the acre” and for the business functions of farms. As shown in Figure 1 below, AI can add value in a wide range of areas for the agriculture sector.
Figure 1. McKinsey. Generative AI can add significant value in R&D, marketing and sales, agronomy and sustainability, and operations.
The application of agriculture technology can see huge benefits, but the sector over the past few years has seen somewhat of an investment drought. McKinsey also notes that “Venture capital (VC) funding has declined by 60 percent since late 2021, due to broader market uncertainty and decreased risk appetite among investors.” However, McKinsey also explains that “the long-term industry outlook remains promising,” and that “analyzed over a ten-year period, Agtech funding continues to remain robust.”
As shown in Figure 2 below, Agtech VC activity in 2024 started off low, with AgtechNavigator reporting this as a “correction” in the market from highs throughout 2021 and 2022.
Figure 2: AgTechNavigator. Agtech VC deal activity by quarter.
Some of the slowdown in funding may simply be a “coming down” from a peak in 2021 because of the COVID-19 pandemic, with funding towards the end of 2023 still higher than the same period in 2020 and could also be due to an “overheated and inflated VC market” overall. The Pitchbook Q3 Agtech Report also notes that “Agtech VC deal value grew for the second consecutive quarter in Q3, signaling a recovery from the lows seen in early 2024.”
Moreover, institutional investors have begun to show renewed interest in Agtech, particularly in startups focused on regenerative agriculture, alternative proteins, and AI-enhanced supply chain logistics. These areas align with global trends in sustainability and food innovation.
Despite VC trends, the industry’s potential is significant. In particular, the agriculture sector “benefits from digitization and the adoption of advanced technologies, including robotics, biotech, and generative AI,” with food security and inflation becoming an increasing global priority considering pressures due to climate change. (See cocoa prices as an example!)
Sustainability-focused technologies also show investment potential, as the agricultural sector becomes the focus of emissions reduction efforts. Agriculture Dive also says that there has been a “boom in interest for artificial intelligence and innovative algorithm-based tech,” and that links between climate tech and Agtech are also driving new investment. With agriculture sector businesses poised to take on the challenge of feeding the world’s growing population, investment in this area could have significant financial as well as societal benefits.
The Role of AI in Crop Management
Crop management is a process by which crops are optimized in their planting, harvesting, and watering, to ensure maximum healthy growth. With precision agriculture, AI can collect data from sensors in the fields, as well as drone and satellite information, and use this data in real time to improve decision-making for crop management processes. This data includes soil composition measurements from soil sensors, water level measurements, weather patterns, and information on pests and diseases. The application of AI data analysis for these approaches can lead to higher crop yields, reduced expenses, and increased efficiency for farmers.
One notable advantage of AI-driven crop management systems is their ability to tailor solutions to specific microclimates and soil profiles. These hyper-localized approaches ensure that interventions are as effective and efficient as possible, minimizing the environmental footprint while maximizing output.
Part of the reason why precision farming and the impacts on crop yield are so important is that arable (usable) land in the world is limited, while the global population is increasing. This means that crop yields need to be maximized as much as possible. Research from the Business Research Company has found that the AI-driven precision farming market is likely to grow over the next few years at a compound annual growth rate (CAGR) of 20%, as shown in Figure 3 below:
Figure 3: The Business Research Company. AI in Precision Farming Global Market Report
Ark Invest notes that the use of AI and precision agriculture is likely to “reduce annual agricultural operating costs by more than 22% globally,” reducing cost pressures on farmers and increasing revenues. As Figure 4 displays, the application of AI-based tools can lead to reduced operating costs for corn, soybeans, and wheat, by reducing costs in numerous aspects of the production cycle, including labor, repairs, and greater efficiency in the use of seeds, fertilizer, and other chemicals.
As operational costs decline, smallholder farmers—who constitute a significant proportion of global agricultural output—are likely to see transformative economic benefits. This democratization of advanced farming technologies could bridge yield gaps between developed and developing nations.
Figure 4: Ark Investment Management. Forecast Reduction in Operating Costs Due to AI and Precision Agriculture.
AI tools can also be used to assist with disease and pest detection for crops, allowing early intervention and damage prevention. They can also allow crops to become resistant to disease in the first place, and improve yields as well, with AI-driven genetic development of seeds. Large amounts of data can be used to determine the highest quality seeds that are high-yield, disease-resistant, or will perform well in changing conditions such as more frequent droughts due to climate change.
Additionally, advancements in AI-guided CRISPR gene-editing technologies are enabling the development of crops with enhanced resilience to abiotic stresses, such as salinity and temperature extremes, further ensuring food security.
Yield forecasting is another important area of crop management and is critically important for food security. If crop yield prediction is likely to drop, governments and organizations can take early steps to prevent shortages. Yield forecasting also allows imports and exports to be managed. For farmers themselves, forecasting prevents resource wastage and allows better financial management.
When using AI tools to support yield forecasting, predictive power can be significantly increased. This is particularly important as climate change and rapidly transforming agricultural practices leave traditional models with historical data inadequate for the task. AI in yield forecasting makes use of Internet of Things (IoT) devices and other advanced sensors, as well as the use of satellite and weather data, allowing predictions to be markedly more accurate, more granular, and able to be adapted in real time. The World Economic Forum notes that growing amounts of data will be produced from farms, allowing AI to be continually improved, as you can see in Figure 5 below:
Figure 5: World Economic Forum. Estimated Amount of Data Generated by the Average Farm Per Day
A paper in the International Journal for Multidisciplinary Research (IJFMR) describes the scope of AI in crop yield forecasting as “vast and promising”, and notes that richer datasets, as well as the development of localized solutions, will allow a more sustainable, predictable and resilient agricultural sector to develop, despite the pressures and challenges of our transforming world.
Several startups in this space are showing how AI-enabled Agtech can be applied, and the interest in the sector. One startup, CarbonRobotics, has developed a laser weeder that uses AI and computer vision to find weeds in real time and kill them. CarbonRobotics raised $70 million in its latest round of funding in 2024, including funding from Nvidia’s venture capital arm. Erik Benson, managing director at Voyager Capital said that CarbonRobotics was “the fastest growing startup he’s seen in 26 years as a venture capitalist.”
Other startups like Plantix are already implementing AI tools for crop diseases, using photo tools to analyze images and provide a diagnosis as well as a solution. These types of solutions will become increasingly widespread as tools gain funding and farmers are able to integrate them into their typical practices.
AI in Livestock Management
Like in crop management, AI can also be used to support and enhance livestock management practices. This includes improving animal health monitoring, breeding optimization, and automated systems for feeding and milking. This is also called “Precision Livestock Farming”. These practices can improve sustainable and resource-optimized management of livestock and can also contribute to greater quality of life and health for livestock due to earlier disease and injury detection. As shown in Figure 6 below, there are numerous uses for AI and increased digitization for livestock management:
Figure 6: Research in Veterinary Science, Arshad et al (2024). Advanced sheep farming using digitization and AI.
Regarding animal health detection, AI systems are increasingly being developed for their efficiency and real-time monitoring tools. These systems can not only support the health and quality of life of individual animals but can potentially also prevent outbreaks among herds. Sensors can track body temperature, activity levels, heart and breathing rates, as well as food intake. One predictive model could “predict the risk of lameness in dairy cows based on accelerometry data, with over 90% accuracy.”
AI in livestock health monitoring also includes advancements in wearable devices. Smart collars equipped with GPS and health trackers provide farmers with real-time data on location, behavior, and health metrics. These collars can alert farmers to signs of distress or potential illness, enabling timely intervention. Moreover, drones equipped with thermal imaging cameras are increasingly being used to monitor livestock health remotely, particularly in large or hard-to-reach pastures.
An article published by Springer Nature found that “research has demonstrated the ability of AI to identify respiratory diseases in pigs based on sound data, and to detect mastitis in dairy cows based on milk conductivity measurements.”
AI can also be used for breeding optimization, with tools determining high-quality breeding pairs, as well as predicting the best time for breeding or artificial insemination. Genomic selection techniques can be used to determine disease resistance, milk quality, or growth speed. The breeding of animals that have been genetically selected with precision can reduce healthcare costs or increase profits from increased milk or meat production. One study found that there have already been “successes in ‘editing out’ disease-causing genes or ‘editing in’ genes that produce high-yielding, disease-resistant animals.”
In addition to improving productivity, AI-driven genomic tools can contribute to sustainability by promoting biodiversity. For example, genetic analysis can identify traits in indigenous breeds that make them resilient to local climates, preserving genetic diversity while enhancing productivity. Such approaches are crucial for combating the challenges posed by climate change.
Automated feeding and milking systems are also becoming more widespread. Cows can enter an automated milking system of their own accord, be milked, and then the milk is sent to storage tanks. These automated systems reduce labor costs and have seen increasing investment over the past several years. The robotic milking market, for example, is expected to grow at a CAGR of 10.8% between 2024 and 2029, according to Markets and Markets, as shown in Figure 7 below:
Figure 7: Markets and Markets. Milking Robots Market Global Forecast to 2029 (USD BN)
This increase is expected because automated milking systems are increasingly accepted globally, and automation is leading to reduced costs in the dairy market more generally. The dairy market is also expected to continue growing, particularly due to the impact of growing populations in China and India.
Furthermore, advancements in AI-powered automated feeding systems ensure precise nutrient allocation based on the specific needs of each animal. These systems analyze data on age, weight, milk production, and health to optimize feed composition, minimizing waste and improving overall livestock health. Such precision in feeding not only enhances productivity but also reduces the environmental footprint of livestock farming.
One example of a startup in the livestock management space is Keymakr, which produces AI-driven image annotation software. This can be used for labeling livestock for management purposes, such as health monitoring, herd counting, abnormal behavior detection and movement detection.
Deloitte has also partnered with other companies and organizations to make AI4Animals, a technology that uses AI to monitor animal handling in slaughterhouses, preventing animal welfare issues. Other startups like Connecterra use AI-driven farm summaries and data analysis to provide information like “daily milk yield per cow”. This can improve feed efficiency, animal welfare, and yield forecasting, and can improve decision-making and profitability for farmers as a result.
AI in Supply Chain and Distribution
In addition to the management and production side of agriculture, supply chain logistics and market monitoring also benefit from the adoption of AI by the sector. As shown in Figure 8, Grand View Research finds that the use of AI in supply chains is continually increasing and is likely to grow as a market sector.
Figure 8: Grand View Research. U.S. Artificial Intelligence in Supply Chain Market Size
Regarding agriculture in particular, supply chain optimization and distribution improvements can have a large impact. In the food industry, delivery optimization, route planning, supply chains, and cold chains can be tracked, monitored, and made more efficient with AI models and predictions. Sensors can determine the best delivery routes in real time and can also reduce transportation costs.
AI-driven predictive analytics are revolutionizing supply chain risk management. By analyzing weather patterns, geopolitical events, and other variables, AI can predict potential disruptions and suggest proactive strategies. For instance, during extreme weather events, AI systems can reroute shipments or recommend alternative suppliers to minimize delays and losses.
The UN says that globally, “around 13.2 percent of food produced is lost between harvest and retail”. AI applications in supply chains can help to prevent food waste and deterioration before it even hits the shelves, for example by improving cold-chain management. Intelligent cold chain systems can use AI, IoT, digital twins, and blockchain to monitor systems, automate temperature control, and ensure supply chains’ integrity and traceability. An article in Computer Science & IT Research Journal published in 2024 found that “AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture.”
Beyond loss prevention, AI-powered cold chain solutions can ensure compliance with stringent food safety regulations. These systems maintain digital logs of temperature and humidity throughout the supply chain, providing detailed audit trails for regulatory inspections and enhancing accountability.
Other AI systems can be applied at the supermarket end of the chain to reduce additional food waste after food reaches consumers. This can be done, for example, through algorithms that automatically keep track of inventories and best-before dates, and reduce prices as an item comes close to being overdue.
As shown in Figure 9 below, 76% of surplus food comes from perishable items, meaning that inefficient management and supply chains can lead to heavy losses. In two pilots done with the World Wildlife Fund (WWF), in collaboration with the Pacific Coast Food Waste Commitment (PCFWC), Afresh, and Shelf Engine, AI was used at supermarkets to improve profits and reduce waste.
The result of these pilots found that in each store, food waste was reduced by 14.8% on average. This was done by using AI to analyze data and make purchasing and ordering more accurate, reducing over-stocking and aligning supplies better with demand. In addition, AI solutions also “increased labor efficiency by up to 20 percent per store,” allowing stores to focus on other tasks.
Figure 9: World Economic Forum. The foods we throw away.
For farmers, AI can also be used to predict market demand and price trends to assist decision-making. Real-time pricing for crops and products can help farmers make the best decisions about when to sell, and AI analysis of market trends can also help farmers to reduce risks related to market fluctuations. Some new projects like the IBM Food Trust help smallholder farmers to gain higher returns on sales, and gain access to new markets that were previously unavailable. One of the managers of the project explains that it is an “important test of how AI and blockchain technology can advance social good and support sustainability by helping even small-scale producers.”
Moreover, AI can facilitate direct-to-consumer models by connecting farmers with end-users through digital marketplaces. These platforms, powered by AI algorithms, match supply with demand, optimize pricing, and reduce intermediaries, ensuring farmers receive fairer compensation.
In addition, numerous global risks and challenges can put supply chains under pressure, as learned during the COVID-19 pandemic. When it comes to the food supply, disruptions can be costly both economically and in terms of human health and life. The application of AI in supply chains can help to predict and buffer risks and use large datasets to make supply chains more resilient to shocks or long-term disruptions.
Several startups are already active in the supply chain space, such as AgriDigital and Intello Labs. AgriDigital focuses on building increased transparency and accountability using blockchain. As shown in Figure 10 below, blockchain has a number of potential uses in the agriculture sector that could be expanded upon:
Figure 10: Urban Vine. Blockchain In Agriculture: 10 Possible Use Cases
Another startup, Intello Labs focuses on another aspect of the supply chain and has produced several tools such as AI-driven shelf monitoring and quality checks, as well as an automated packing and weighing machine run by AI which increases packing speed ten-fold. Intello Labs most recently raised $2.82 million in a Series B round in 2022.
Key Challenges and Barriers
While the application of AI to the agriculture industry can have many benefits for the food sector and for society at large, there are also several challenges for this technology.
First, AI tools are expensive and farmers may be slow to take up new technologies due to lack of future funding, lack of policy frameworks, and general uncertainty. A UK study undertaken in 2022 found that “farmers in general are open to new technologies and many are already seeing benefits, although the full potential of precision tools is yet to be grasped in the farming community. As expected, younger generations were the most optimistic about data-driven farming systems boosting productivity and growth.” In the U.S., in contrast, “AI take-up in the past few years has been swift,” with BBC reporting that “87% of businesses in the US agricultural industry were using AI in some shape or form as of late 2021”. Providing additional government funding may be a key aspect of this uptake.
In developing countries, this inequality may be more visible when it comes to a lack of data access. With lower levels of digitization, access to the internet and access to sensors including smartphones, developing countries could struggle to take up these types of technologies that benefit wealthier nations. The disparity also extends to the availability of technical expertise, as many farmers in developing regions lack the skills or training to implement AI solutions effectively. Collaboration with international organizations to establish training programs and subsidized technology packages can help alleviate this challenge.
Another key challenge, like with AI in all sectors, is that of privacy and security. As more data gets incorporated into agricultural AI systems, the “threat landscape” increases. In addition, there are few regulations and laws around farm data specifically, and the use of AI tools in agriculture could potentially lead to privacy issues for farmers themselves. While farm data itself might seem anonymous, the combination of different datasets accessed through breaches or data loss could expose a farmer’s identity. As a result, data protection guidelines need to be developed for the sector to reduce these risks, and technology providers need to develop robust transparency, disclosure, and accountability mechanisms. Efforts to create industry standards for data management and secure cloud-based solutions could also play a pivotal role in ensuring the ethical use of agricultural data.
In addition, for AI tools to function correctly, they need reliable information, which in many agricultural use cases is region-specific. In addition, agricultural data is often dynamic and constantly changing, such as weather data, soil data, disease spread, climate conditions, food demand and more. Simple interfaces need to be developed to allow these data types to be monitored and assessed effectively. Data used for these models is still fragmented and often siloed, making it difficult to combine datasets for maximum benefit. Projects like the DEMETER project in the EU are aiming to improve interoperability so that farmers can access more data with higher quality.
The image from the World Bank in Figure 11 below shows some of the data flows in the agriculture sector when more technology is involved:
Figure 11: World Bank. Efficient data flows critical for Digital Agriculture.
As shown, the efficiency of data flows depends on a combination of sensors, connectivity infrastructure, data management, analysis, business services, and IoT-specific practices. To support these, standards, data governance, regulations and policies also still need to be developed and more widespread. Additionally, initiatives to develop low-cost sensors and robust rural connectivity solutions, such as satellite-based internet, could be transformative in regions with limited infrastructure.
Furthermore, the data used to train models may be imbalanced, and it can be difficult to generate more balanced datasets. For example, crop disease models could be trained primarily on images of healthy leaves, leading them to be less able to identify diseased leaves, and tending towards classifying leaves as healthy, even if this is not the case. Projects like the STELAR project already recognize these types of issues, however, and are working to alleviate them. A focus on crowdsourced data collection and partnerships with universities could help diversify datasets, improving model accuracy and reliability across varied agricultural environments.
Impact on Sustainability and Climate-Resilient Agriculture
The U.S. Department of Agriculture estimates that the agriculture sector is responsible for approximately 10.6% of US greenhouse gas emissions (2021). This can be seen in Figure 12 below:
Figure 12: U.S. Department of Agriculture. Estimated U.S. greenhouse gas emissions by sector, including electricity use, 2021.
These emissions include methane gases from animals, the release of nitrous oxide from fertilizer application and manure storage and management, and electricity use for farming processes. As a result, improvements to sustainability and reductions in carbon footprint for the sector can have a large impact on overall greenhouse gas emissions in the country. Addressing these emissions will also align with global sustainability goals, such as the United Nations Sustainable Development Goals (SDGs), particularly those focused on climate action and sustainable agriculture.
The use of AI-enabled tools can help to improve sustainability in the sector, by improving water resource management, reducing carbon emissions and pollution from agricultural processes, and increasing resilience to climate change and extreme weather events. AI also has the potential to enhance biodiversity by identifying and protecting critical habitats within farmlands, ensuring a balance between productivity and ecological conservation.
First, water resource management can be improved through optimizing irrigation systems and using water only when it is needed. An article published in Nature in 2024 highlighted how AI and remote sensing technologies can significantly improve irrigation efficiency with precise demand forecasting. Deep learning and neural networks, and the integration of new data sets, can improve existing models to anticipate agricultural water demand, reducing wastage. By integrating real-time weather data and soil moisture levels, these systems can ensure irrigation schedules align with actual crop requirements, further reducing water use.
The carbon footprint of the agriculture industry can also be reduced in other ways, such as reducing fertilizer and pesticide use, which in turn reduces the emission of nitrous oxide. Scientists at Imperial College London found that AI tools and smart sensors can determine more accurately the level of ammonium in the soil (which is converted to nitrites and nitrates), reducing the likelihood of fertilizer overuse. This is particularly important as excess nitrogen fertilizer releases greenhouse gases, as well as polluting air and waterways. Combining AI with regenerative agriculture practices, such as crop rotation and cover cropping, could further enhance soil health and minimize chemical inputs.
One study carried out by the Hong Kong University of Science and Technology also found that the use of AI tools could reduce global ammonia emissions by 38%, an important step for reducing pollution in waterways that comes from farm runoff. As shown in Figure 13, for the U.S. the researchers found that it had both high ammonia emissions and high reduction potential, particularly for corn:
Figure 13. Hong Kong University of Science and Technology. NH3 emission maps and mitigation potential of three major crops.
Finally, the use of AI tools in the agriculture sector can more broadly increase resilience to climate change, through predicting weather events that could disrupt crop growth or create dangers to livestock.
For example, the Flood Hub AI tool was able to accurately predict floods in South Africa’s Western Cape, and other tools like PlantVillage were able to detect plant diseases early. Technologies like these use predictive models and real-time data to increase resilience and mitigate risks, such as wildfire and drought risks, flash flooding risks, and broader ecosystem health or collapse. Scaling these tools across regions and incorporating additional localized data can enhance their effectiveness in diverse agricultural contexts.
Smart water management has also been employed by Microsoft in their FarmVibes project to predict water shortages, allowing farmers to collect and stockpile water in advance of those times. Another startup, CropX, uses weather and soil data combined with AI analysis to improve irrigation, disease tracking, nutrition and effluent management. Their irrigation planning tools can help to save water, while making sure that plants receive moisture before showing signs of stress. Collaborations with local governments and agricultural cooperatives could ensure that these innovations reach smallholder farmers, amplifying their impact.
Future Growth Opportunities
There are several future growth opportunities for AI technology in the agriculture industry and areas that are poised to expand and develop further soon. This includes autonomous farming equipment, increasing use of IoT and smart farming, and the creation of new agriculture technologies such as alternative proteins and lab-grown meat, as well as urban agriculture. These advancements align with global trends towards food security, environmental sustainability, and technological integration.
When it comes to AI-powered robotics for planting, harvesting, and weeding, several technologies are already in development and are starting to become more widespread. For example, John Deere is already working with the startup Agtonomy to build autonomous and AI-enabled equipment. Some tractors will already be autonomous by 2025, and there are plans to have fully autonomous corn and soy production systems by 2030. As these technologies evolve, integrating features such as real-time remote monitoring and predictive maintenance will further enhance their efficiency and reliability.
Recent startups like Blue River Technology have also developed precision technologies to improve weed spraying approaches, using computer vision, machine learning and robotics to ensure that only the necessary amounts of pesticides are used, and no more. This approach not only reduces chemical use but also minimizes the environmental impact of farming practices, paving the way for more sustainable food systems.
Autonomous pollinating drones are becoming more popular as well. Patents filed by Walmart go back as far as 2018, but the widespread use of such equipment is still to come. Companies like QueenDee have already started to use AI-enabled drones to monitor and control pollen distribution within greenhouses. A project is also being undertaken by the UK Agri-Tech Centre in collaboration with other companies such as Polybee, to test drone pollination for commercial strawberry production in the UK. Further research into AI-guided pollination can expand its applicability to a broader range of crops, supporting global food security initiatives.
Lab-grown meat investment opportunities are also growing rapidly, with investment in 2020 more than 6 times higher than in 2016, as shown in Figure 14 below:
Figure 14: Visual Capitalist. Capital flow into the cultured meat industry has exploded in recent years.
While investment dropped in 2022 and 2023 for cultured meat, a number of regulatory approvals were also granted in several countries. This paves the way for additional growth and investments, with all-time investment in the new industry already reaching $3.1 billion. Collaborative partnerships between biotech firms and AI developers could accelerate breakthroughs in scaling production and reducing costs, making lab-grown meat more accessible to consumers.
Cultured meat can reduce greenhouse gas emissions as well as land use. Companies like BioCraft Pet Nutrition are already using AI and machine-learning tools to accelerate research for cultivated meat pet food. Others like Aleph are collaborating with AI platforms like BioRaptor to improve data analysis processes in their R&D. This technological synergy has the potential to revolutionize the protein industry, catering to growing consumer demand for ethical and environmentally friendly food sources.
Urban agriculture is also expanding as one way to increase food security for urban areas and to deal with reduced land surface area available for growing. It also improves climate resilience by distributing agricultural production in a more diverse way.
One increasingly popular approach for urban farms is vertical farming. Vertical farming technologies are not new, but failed to become widespread due to high startup costs, complex logistics, and high energy demands. That may be poised to change, with the support of AI-driven vertical farms, which use IoT sensors and automated processes to optimize plant growth and manage technical processes that have otherwise made vertical farms too complex. ZERO, a startup covered by Forbes in 2024, uses AI technologies to manage vertical farms, as well as creating other ground-breaking new technologies such as modular farming, which they say “enables farmers to grow anything, anywhere.” As energy efficiency technologies advance, integrating renewable energy sources into vertical farming systems will further enhance their viability and sustainability.
Case Studies
Several case studies have shown the effectiveness of AI in improving agricultural processes and revolutionizing traditional farming. For example, a case study in 2024 with a computer vision AI solution Cainthus (acquired by EverAg), found that feed efficiency could be increased by 4%, with a 43% reduction in average low feed hours for cattle who were monitored with the AI solution. The solution works by optimizing inputs of cattle feed: when feed efficiency increases, cows eat less while sustaining milk production, allowing farmers to save on costs. They also found a 4% increase in cow comfort, by trialing different barn designs because of the AI analysis. These findings highlight the dual benefits of AI: improving productivity while simultaneously enhancing animal welfare.
The AI4AI initiative undertaken by the World Economic Forum in India also found that AI tools could have a huge impact on farmers. By using robot advisory services, soil testing sensors, AI-based quality testing and a digital platform, farmers were able to double their income (to around $800 USD per acre). They also found that “the digital advisory services contributed to a 21% increase in chili yield production per acre. Pesticide use fell by 9% and fertilizers dropped by 5%, while quality improvements boosted unit prices by 8%.” The case study was such a success that it was expanded from 7000 farmers to 500,000 by the state government in India.
AgroScout, a startup noted by Google for its crop-monitoring technology, completed a potato quality management case study in which potato crop in Israel was monitored with aerial data measurements. These frequent and high-quality measurements allowed harvest times and yield to be determined more precisely, leading to improvements in yield estimates and harvest time accuracy by 10%. Such precision farming techniques demonstrate how AI can enable better decision-making and reduce resource waste, ultimately contributing to more sustainable agricultural practices.
Conclusion
AI has a large role to play in the transformation of the ag sector, and the ability to contribute significantly to the food security and nutrition of the world’s rapidly growing population. AI tools can boost crop yields and livestock productivity, increase the resistance of seeds and crops to diseases and pests, and overall make farming more efficient, productive, and sustainable. As these technologies mature, their integration with blockchain and other digital tools can enhance traceability and consumer trust in agricultural products.
Numerous autonomous technologies are in the process of being developed and trialed, and the adoption of IoT and smart technology in farming is becoming widespread. The development of new food sources such as lab-grown meat, vertical farming, and other approaches are also being supported by AI technologies, data analysis, and sensor-based monitoring. Moreover, global partnerships between private and public sectors will be crucial in creating the infrastructure and policies needed to scale these innovations effectively.
While the industry has faced some challenges in both investment growth and uptake over the past few years, these trends are likely to do an about-face as technology becomes cheaper, more available, and increasingly able to fill the niches that farmers need additional support in.
